dagshub-docs/docs/integration_guide/google_colab.md

#Google Colab Google Colaboratory{:rel="nofollow" target="_blank"} , or "Colab" for short, is a free Jupyter notebook environment that runs entirely in the cloud. It does not require any setup, can be shared easily with team members, and provides free access to GPUs. DagsHub provides its users with Colab Notebook templates for various tasks, such as fully configuring DagsHub with Colab runtime, transferring data from Google Drive to DagsHub Storage, tutorials, and more.

How do DagsHub works with Google Colab?

You can easily access your DagsHub remotes using Git, DVC, and MLflow from any Colab environment. Colab allows its users to download files to your runtime using API calls and bash commands. By setting your DagsHub credentials, you'll be able to clone the Git server, pull the data hosted on DagsHub Storage and log experiments to the project's MLflow Tracking server.

How to use DagsHub with Google Colab?

DagYard

With this notebook, you can easily pull all of your project’s components from DagsHub to Colab runtime, train the model, log the experiments, version the changes, and push them to DagsHub remotes. You need to simply check some boxes, fill in your details, run the notebook, and you’re set to go! Behind the scenes, it will configure your DagsHub project with Colab and pull the project components you choose to the run time. From the DagYard cell on, you will be able to work on your project, version the changes with Git and DVC, and easily push them to the DagsHub remotes.

The DagWalker

Follow this notebook to copy and version directories from Google Drive to DagsHub storage using DVC.

Tutorials

• Hello World{:rel="nofollow" target="_blank"} - Try DagsHub without installing anything locally. The primary goal of this notebook is to help you learn the basic features and usage of DagsHub while maintaining a relatively clean environment. By following this notebook, you will create your first hello-world project on DagsHub.
• Tensorflow{:rel="nofollow" target="_blank"} , fast.ai{:rel="nofollow" target="_blank"} - Learn how to log MLflow Experiments to your DagsHub's MLflow Tracking server by following a few steps.
• DagsHub x GitHub{:rel="nofollow" target="_blank"} - Learn how to use all the benefits DagsHub has to offer in your GitHub project by following this notebook.
• DagsHub Tracking{:rel="nofollow" target=_blank} - Using DagsHub Tracking to log fully reproducible experiments.